The present invention relates generally to data processing systems and methods, and more particularly to systems and methods for determining the end of the baseline in an amplification curve, such as in a polymerase chain reaction (PCR).
Many experimental processes exhibit amplification of a quantity. For example, in PCR, the quantity may correspond to the number of parts of a DNA strand that have been replicated, which dramatically increases during an amplification stage or region. PCR data is typically described by a region of linear drifting baseline which is a precursor to exponential growth of PCR amplification. As the consumables are exhausted, the curve turns over and asymptotes. It is desirable to remove the linear drift as much as possible and baseline the signal to zero. For accurate baselining, the beginning and end of the baseline require identifying as accurately as possible. Once this is done, then a linear fit to the points between the begin and end cycles can be subtracted off from the data.
One prior art technique for determining the end of the baseline is as follows. The first pair of points in the curve are selected. For the first point, the first point plus the next three points of the curve are selected for processing. Using these four points, a linear least squares fit is determined and the slope calculated. This process is repeated for the second point. If the change in the two slopes is greater than a fixed number, that is, if the curve turns upward signaling amplification, then the process stops with the end cycle having been determined. If the change in the slops is not greater than the fixed number, the process continues for the next pair of points (e.g., the second and third points in the curve). The process is repeated until a change in slope is greater than the fixed number.
One problem with this approach is that using just four points to define the linear fit is extremely sensitive to small variations in the amplitude of a single point. Spikes or fluctuations due to noisy data will often cause the process to truncate prematurely, resulting in a poor baselining. Moreover, the threshold for the change in slopes is an arbitrary number, which may have to be modified as the data and instrument change. This calibration costs additional time and money. Further, since the algorithm stops when a fixed condition is met, there is no opportunity to analyze the remaining part of the curve to determine if there is a better stopping point.
Therefore it is desirable to provide systems and methods for determining the end cycle of the baseline in an amplification curve that overcome the above and other problems.